Biomedical subjects

Benjamin G Tehan

Publications and source records attributed to Benjamin G Tehan.

3 recordsLinked to original sources

PubMed2004

Comparisons of the HBV and HIV polymerase, and antiviral resistance mutations.

The antiviral treatment of chronic hepatitis B is limited by the selection of antiviral resistance mutations. Primary resistance to lamivudine occurs at rtM2041/V in the C Domain of the polymerase. Recently, resistance to adefovir has also been described in the D Domain at rtN236T. The treatment of patients with resistant virus without complete suppression can lead to the further selection of compensatory mutations. Thus, to gain an understanding of the hepatitis B virus (HBV) polymerase and also mutations associated with resistance, a three-dimensional model of the HBV reverse transcriptase core region based on homology with human immunodeficiency virus (HIV) was created. A comparative analysis of the HIV polymerase and the model of HBV polymerase was performed. In addition, the antiviral resistance mutations including potential compensatory mutations were mapped to determine their effect on the HBV polymerase model, especially in the nucleotide binding site.

Amino Acid Sequence↗

PubMed2002

Molecular mapping in the CNS.

Since ancient times the operation of the brain has elicited more than usual interest. Data mining of the human genome is revealing that many CNS abnormalities have a genetic component. As yet this information can not be used directly to cure or ameliorate specific CNS disorders although this is regarded as having great potential for future therapies. Current CNS drug design and 3D QSAR is based on knowing either the structures of key proteins and how smaller molecules interact with them to obtain a pharmacological response, or on hypothesising about key structural features and interactions by a variety of molecular modelling and computational techniques. Methods used include conformational analyses, pharmacophore development and QSAR which are now being actively applied to increase our understanding of how molecules interact with specific sites within the CNS as a basis for the design of new pharmacologically active compounds. In this review we give an overview of the latest strategies used in 3D-QSAR based drug design and survey the most recent applications of these strategies to the CNS. By way of example, accounts are given of computer-based research aimed at drugs targeting GABA, glutamate, dopamine and opioid receptors.

Central Nervous System Agents↗

PubMedDate not supplied

A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.

BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.

Journal Article↗